In today's digital era, social networks have become a key part of everyday human life, where people expose their opinions, preferences, and social interactions. Online continuous living creates a treasure of digital footprints, which can provide very useful information in the study of personality traits. We delve into actual data to examine how these patterns of behavior input into predicting personality traits. This work explores the opportunities offered by social media as a dataset for personality assessment by various methods of machine learning as classification, clustering, and deep learning. Using the Big Five model of personality, we categorize people with references to Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The collected data from social media networks provided a huge dataset that is utilized in training predictive models and modelling online habits interacting and the sentiment trends. The comparative analysis shows machine learning to be superior to conventional self-report personality measures, whereby the machine-learned models are scalable and automated, objective personality tests capable of delivering great information in minimal time. Especially the deep learning algorithms based on transformer architectures show extremely high accuracy predictions, in the range of 75-85%.This technology also comes wrapped in a plethora of ethical problems, primarily about consent, algorithmic bias, and privacy. In this paper, we discuss these problems and propose guidelines for the benevolent execution of such technology. This is a wide-ranging process that can constitute anything from targeted advertisements and hiring decisions to following up on an individual's mental health and also human-computer interactions. This study allows one to understand how AI can help understand human behaviors, paving the way for a more effective and ethical use of machine learning in social and behavioral sciences.
personality prediction, Big five inventory, Myers-Briggs Type Indicator, OCEAN Model
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